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2020-09-14
Sivaram, M., Ahamed A, Mohamed Uvaze, Yuvaraj, D., Megala, G., Porkodi, V., Kandasamy, Manivel.  2019.  Biometric Security and Performance Metrics: FAR, FER, CER, FRR. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). :770–772.
Biometrics manages the computerized acknowledgment of people dependent on natural and social attributes. The example acknowledgment framework perceives an individual by deciding the credibility of a particular conduct normal for person. The primary rule of biometric framework is recognizable proof and check. A biometric confirmation framework use fingerprints, face, hand geometry, iris, and voice, mark, and keystroke elements of a person to recognize an individual or to check a guaranteed character. Biometrics authentication is a form of identification and access control process which identify individuals in packs that are under reconnaissance. Biometric security system increase in the overall security and individuals no longer have to deal with lost ID Cards or forgotten passwords. It helps much organization to see everyone is at a certain time when something might have happened that needs reviewed. The current issues in biometric system with individuals and many organization facing are personal privacy, expensive, data's may be stolen.
Ma, Zhuo, Liu, Yang, Liu, Ximeng, Ma, Jianfeng, Li, Feifei.  2019.  Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices. IEEE Internet of Things Journal. 6:8406–8420.
Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human-machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.
2020-09-11
Prokofiev, Anton O., Smirnova, Yulia S..  2019.  Counteraction against Internet of Things Botnets in Private Networks. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :301—305.
This article focuses on problems related to detection and prevention of botnet threats in private Internet of Things (IoT) networks. Actual data about IoT botnets activity on the Internet is provided in the paper. Results of analysis of widespread botnets, as well as key characteristics of botnet behavior and activity on IoT devices are also provided. Features of private IoT networks are determined. The paper provides architectural features as well as functioning principles of software systems for botnet prevention in private networks. Recommendations for process of interaction between such system and a user are suggested. Suggestions for future development of the approach are formulated.
Eskandarian, Saba, Cogan, Jonathan, Birnbaum, Sawyer, Brandon, Peh Chang Wei, Franke, Dillon, Fraser, Forest, Garcia, Gaspar, Gong, Eric, Nguyen, Hung T., Sethi, Taresh K. et al..  2019.  Fidelius: Protecting User Secrets from Compromised Browsers. 2019 IEEE Symposium on Security and Privacy (SP). :264—280.
Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.
2020-09-04
Karim, Hassan, Rawat, Danda.  2019.  A Trusted Bluetooth Performance Evaluation Model for Brain Computer Interfaces. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). :47—52.
Bluetooth enables excellent mobility in Brain Computer Interface (BCI) research and other use cases including ambulatory care, telemedicine, fitness tracking and mindfulness training. Although significant research exists for an all-encompassing BCI performance rating, almost all the literature addresses performance in terms of brain state or brain function classification accuracy. For the few published experiments that address BCI hardware performance, they too, focused on improving classification accuracy. This paper explores some of the more recent studies and proposes a trusted performance rating for BCI applications based on the enhanced privacy, yet reduced bandwidth needs of mobile EEG-based BCI applications. This paper proposes a set of Bluetooth operating parameters required to meet the performance, usability and privacy requirements of reliable and secure mobile neuro-feedback applications. It presents a rating model, "Trusted Mobile BCI", based on those operating parameters, and validated the model with studies that leveraged mobile BCI technology.
Zhao, Zhen, Lai, Jianchang, Susilo, Willy, Wang, Baocang, Hu, Yupu, Guo, Fuchun.  2019.  Efficient Construction for Full Black-Box Accountable Authority Identity-Based Encryption. IEEE Access. 7:25936—25947.
Accountable authority identity-based encryption (A-IBE), as an attractive way to guarantee the user privacy security, enables a malicious private key generator (PKG) to be traced if it generates and re-distributes a user private key. Particularly, an A-IBE scheme achieves full black-box security if it can further trace a decoder box and is secure against a malicious PKG who can access the user decryption results. In PKC'11, Sahai and Seyalioglu presented a generic construction for full black-box A-IBE from a primitive called dummy identity-based encryption, which is a hybrid between IBE and attribute-based encryption (ABE). However, as the complexity of ABE, their construction is inefficient and the size of private keys and ciphertexts in their instantiation is linear in the length of user identity. In this paper, we present a new efficient generic construction for full black-box A-IBE from a new primitive called token-based identity-based encryption (TB-IBE), without using ABE. We first formalize the definition and security model for TB-IBE. Subsequently, we show that a TB-IBE scheme satisfying some properties can be converted to a full black-box A-IBE scheme, which is as efficient as the underlying TB-IBE scheme in terms of computational complexity and parameter sizes. Finally, we give an instantiation with the computational complexity as O(1) and the constant size master key pair, private keys, and ciphertexts.
Kumar, M Ashok, Radhesyam, V., SrinivasaRao, B.  2019.  Front-End IoT Application for the Bitcoin based on Proof of Elapsed Time (PoET). 2019 Third International Conference on Inventive Systems and Control (ICISC). :646—649.
There are some registry agreements that may be appropriate for the Internet of Things (IoT), including Bitcoin, Hyperledger Fabric and IOTA. This article presents quickly and examines them in terms of the progress of Internet applications. Block-dependent IoT applications can consolidate the chain's rationale (smart contracts) and front-end, portable or front-end web applications. We present three possible designs for BC IoT front-end applications. They vary depending on the Bitcoin block chain customer (neighborhood gadget, remote server) and the key location needed to manage active exchanges. The vital requirements of these projects, which use Bitcoin to organize constructive exchanges, are the volumes of information, the area and time of the complete block and block block, and the entry of the Bitcoin store. The implications of these surveys show that it is unlikely that a full Bitcoin distributor will continue to operate reliably with a mandatory IoT gadget. Then, designing with remote Bitcoin customers is, in all respects, a suitable methodology in which there are two minor alternatives and vary in key storage / management. Similarly, we recommend using the design with a unique match between the IoT gadget and the remote blockchain client to reduce system activity and improve security. We hope you also have the ability to operate with versatile verses with low control and low productivity. Our review eliminates the contradictions between synthesis methodologies, but the final choice for a particular registration agreement and the original technique completely depends on the proposed use case.
Kanemura, Kota, Toyoda, Kentaroh, Ohtsuki, Tomoaki.  2019.  Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :154—158.
Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.
Wu, Yan, Luo, Anthony, Xu, Dianxiang.  2019.  Forensic Analysis of Bitcoin Transactions. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :167—169.
Bitcoin [1] as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals.
Glory, Farhana Zaman, Ul Aftab, Atif, Tremblay-Savard, Olivier, Mohammed, Noman.  2019.  Strong Password Generation Based On User Inputs. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0416—0423.
Every person using different online services is concerned with the security and privacy for protecting individual information from the intruders. Many authentication systems are available for the protection of individuals' data, and the password authentication system is one of them. Due to the increment of information sharing, internet popularization, electronic commerce transactions, and data transferring, both password security and authenticity have become an essential and necessary subject. But it is also mandatory to ensure the strength of the password. For that reason, all cyber experts recommend intricate password patterns. But most of the time, the users forget their passwords because of those complicated patterns. In this paper, we are proposing a unique algorithm that will generate a strong password, unlike other existing random password generators. This password will he based on the information, i.e. (some words and numbers) provided by the users so that they do not feel challenged to remember the password. We have tested our system through various experiments using synthetic input data. We also have checked our generator with four popular online password checkers to verify the strength of the produced passwords. Based on our experiments, the reliability of our generated passwords is entirely satisfactory. We also have examined that our generated passwords can defend against two password cracking attacks named the "Dictionary attack" and the "Brute Force attack". We have implemented our system in Python programming language. In the near future, we have a plan to extend our work by developing an online free to use user interface. The passwords generated by our system are not only user-friendly but also have achieved most of the qualities of being strong as well as non- crackable passwords.
Osia, Seyed Ali, Rassouli, Borzoo, Haddadi, Hamed, Rabiee, Hamid R., Gündüz, Deniz.  2019.  Privacy Against Brute-Force Inference Attacks. 2019 IEEE International Symposium on Information Theory (ISIT). :637—641.
Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we derive the optimal utility-privacy trade-off via a linear program with any f-information adopted as the utility measure, and show that the optimal utility is a concave and piece-wise linear function of the privacy-leakage budget.
2020-08-28
Singh, Praveen Kumar, Kumar, Neeraj, Gupta, Bineet Kumar.  2019.  Smart Cards with Biometric Influences: An Enhanced ID Authentication. 2019 International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE). :33—39.
Management of flow of all kinds of objects including human beings signifies their real time monitoring. This paper outlines the advantages accrued out of biometrics integration with Smartcards. It showcases the identity authentication employed through different biometric techniques. Biometric key considerations influencing the essence of this technology in Smartcards have been discussed briefly in this paper. With better accuracy and highly reliable support system this technology finds itself today in widespread deployment. However, there are still some concerns with human interfaces along with important factors in implementations of biometrics with smartcards which have been highlighted in this article. This paper also examines the privacy concerns of users in addressing their apprehensions to protect their confidentiality through biometric encryption and proposes DNA technology as a best possible biometric solution. However, due to inherent limitations of its processing time and an instant requirement of authentication, it has been suggested in the proposed modal to use it with combination of one or more suitable biometric technologies. An instant access has been proposed to the user with limited rights by using biometric technology other than the DNA as a primary source of authentication. DNA has been proposed as secondary source of authentication where only after due sample comparison full access rights to the user will be granted. This paper also aims in highlighting the number of advantages offered by the integration of biometrics with smartcards. It also discusses the need to tackle existing challenges due to restrictions in processing of different biometric technologies by defining certain specific future scopes for improvements in existing biometric technologies mainly against the time taken by it for sample comparisons.
Zobaed, S.M., ahmad, sahan, Gottumukkala, Raju, Salehi, Mohsen Amini.  2019.  ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :609—616.
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.
Zhou, Xiaojun, Lin, Ping, Li, Zhiyong, Wang, Yunpeng, Tan, Wei, Huang, Meng.  2019.  Security of Big Data Based on the Technology of Cloud Computing. 2019 4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). :703—7033.
To solve the problem of big data security and privacy protection, and expound the concept of cloud computing, big data and the relationship between them, the existing security and privacy protection method characteristic and problems were studied. A reference model is proposed which is based on cloud platform. In this model the physical level, data layer, interface layer and application layer step by step in to implement the system security risk early warning and threat perception, this provides an effective solution for the research of big data security. At the same time, a future research direction that uses the blockchain to solve cloud security and privacy protection is also pointed out.
Ferreira, Pedro, Orvalho, Joao, Boavida, Fernando.  2007.  Security and privacy in a middleware for large scale mobile and pervasive augmented reality. 2007 15th International Conference on Software, Telecommunications and Computer Networks. :1—5.
Ubiquitous or pervasive computing is a new kind of computing, where specialized elements of hardware and software will have such high level of deployment that their use will be fully integrated with the environment. Augmented reality extends reality with virtual elements but tries to place the computer in a relatively unobtrusive, assistive role. In this paper we propose, test and analyse a security and privacy architecture for a previously proposed middleware architecture for mobile and pervasive large scale augmented reality games, which is the main contribution of this paper. The results show that the security features proposed in the scope of this work do not affect the overall performance of the system.
2020-08-24
Jeon, Joohyung, Kim, Junhui, Kim, Joongheon, Kim, Kwangsoo, Mohaisen, Aziz, Kim, Jong-Kook.  2019.  Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S). :3–4.
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.
Al-Odat, Zeyad A., Khan, Samee U..  2019.  Anonymous Privacy-Preserving Scheme for Big Data Over the Cloud. 2019 IEEE International Conference on Big Data (Big Data). :5711–5717.
This paper introduces an anonymous privacy-preserving scheme for big data over the cloud. The proposed design helps to enhance the encryption/decryption time of big data by utilizing the MapReduce framework. The Hadoop distributed file system and the secure hash algorithm are employed to provide the anonymity, security and efficiency requirements for the proposed scheme. The experimental results show a significant enhancement in the computational time of data encryption and decryption.
Yuan, Xu, Zhang, Jianing, Chen, Zhikui, Gao, Jing, Li, Peng.  2019.  Privacy-Preserving Deep Learning Models for Law Big Data Feature Learning. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :128–134.
Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.
Cuzzocrea, Alfredo, Damiani, Ernesto.  2019.  Making the Pedigree to Your Big Data Repository: Innovative Methods, Solutions, and Algorithms for Supporting Big Data Privacy in Distributed Settings via Data-Driven Paradigms. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:508–516.
Starting from our previous research where we in- troduced a general framework for supporting data-driven privacy-preserving big data management in distributed environments, such as emerging Cloud settings, in this paper we further and significantly extend our past research contributions, and provide several novel contributions that complement our previous work in the investigated research field. Our proposed framework can be viewed as an alternative to classical approaches where the privacy of big data is ensured via security-inspired protocols that check several (protocol) layers in order to achieve the desired privacy. Unfortunately, this injects considerable computational overheads in the overall process, thus introducing relevant challenges to be considered. Our approach instead tries to recognize the “pedigree” of suitable summary data representatives computed on top of the target big data repositories, hence avoiding computational overheads due to protocol checking. We also provide a relevant realization of the framework above, the so- called Data-dRIven aggregate-PROvenance privacy-preserving big Multidimensional data (DRIPROM) framework, which specifically considers multidimensional data as the case of interest. Extensions and discussion on main motivations and principles of our proposed research, two relevant case studies that clearly state the need-for and covered (related) properties of supporting privacy- preserving management and analytics of big data in modern distributed systems, and an experimental assessment and analysis of our proposed DRIPROM framework are the major results of this paper.
Long, Cao-Fang, Xiao, Heng.  2019.  Construction of Big Data Hyperchaotic Mixed Encryption Model for Mobile Network Privacy. 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). :90–93.
Big data of mobile network privacy is vulnerable to clear text attack in the process of storage and mixed network information sharing, which leads to information leakage. Through the mixed encryption of data of mobile network privacy big data to improve the confidentiality and security of mobile network privacy big data, a mobile network privacy big data hybrid encryption algorithm based on hyperchaos theory is proposed. The hybrid encryption key of mobile network privacy big data is constructed by using hyperchaotic nonlinear mapping hybrid encryption technology. Combined with the feature distribution of mobile network privacy big data, the mixed encrypted public key is designed by using Logistic hyperchaotic arrangement method, and a hyperchaotic analytic cipher and block cipher are constructed by using Rossle chaotic mapping. The random piecewise linear combination method is used to design the coding and key of mobile network privacy big data. According to the two-dimensional coding characteristics of mobile network privacy big data in the key authorization protocol, the hybrid encryption and decryption key of mobile network privacy big data is designed, and the mixed encryption and decryption key of mobile network privacy big data is constructed, Realize the privacy of mobile network big data mixed encryption output and key design. The simulation results show that this method has good confidentiality and strong steganography performance, which improves the anti-attack ability of big data, which is used to encrypt the privacy of mobile network.
Liu, Hongling.  2019.  Research on Feasibility Path of Technology Supervision and Technology Protection in Big Data Environment. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :293–296.
Big data will bring revolutionary changes from life to thinking for society as a whole. At the same time, the massive data and potential value of big data are subject to many security risks. Aiming at the above problems, a data privacy protection model for big data platform is proposed. First, the data privacy protection model of big data for data owners is introduced in detail, including protocol design, logic design, complexity analysis and security analysis. Then, the query privacy protection model of big data for ordinary users is introduced in detail, including query protocol design and query mode design. Complexity analysis and safety analysis are performed. Finally, a stand-alone simulation experiment is built for the proposed privacy protection model. Experimental data is obtained and analyzed. The feasibility of the privacy protection model is verified.
Harris, Daniel R., Delcher, Chris.  2019.  bench4gis: Benchmarking Privacy-aware Geocoding with Open Big Data. 2019 IEEE International Conference on Big Data (Big Data). :4067–4070.
Geocoding, the process of translating addresses to geographic coordinates, is a relatively straight-forward and well-studied process, but limitations due to privacy concerns may restrict usage of geographic data. The impact of these limitations are further compounded by the scale of the data, and in turn, also limits viable geocoding strategies. For example, healthcare data is protected by patient privacy laws in addition to possible institutional regulations that restrict external transmission and sharing of data. This results in the implementation of “in-house” geocoding solutions where data is processed behind an organization's firewall; quality assurance for these implementations is problematic because sensitive data cannot be used to externally validate results. In this paper, we present our software framework called bench4gis which benchmarks privacy-aware geocoding solutions by leveraging open big data as surrogate data for quality assurance; the scale of open big data sets for address data can ensure that results are geographically meaningful for the locale of the implementing institution.
Dong, Kexiong, Luo, Weiwei, Pan, Xiaohua, Yin, Jianwei.  2019.  An Internet Medical Care-Oriented Service Security Open Platform. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :489–492.
As an inevitable trend of information development of hospitals, Internet hospitals provide a series of convenient online services for patients such as registration, consultation, queuing, payment and medicine pick-up. However, hospitals have to face huge challenges, and deploy an Internet medical care-oriented service security open platform to ensure the security of personal privacy data and avoid malicious attacks from the Internet, so as to prevent illegal stealing of medical data. The service security open platform provides visualized control for the unified and standardized connection process and data access process.
2020-08-17
Fischer, Marten, Scheerhorn, Alfred, Tönjes, Ralf.  2019.  Using Attribute-Based Encryption on IoT Devices with instant Key Revocation. 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :126–131.
The Internet of Things (IoT) relies on sensor devices to measure real-world phenomena in order to provide IoT services. The sensor readings are shared with multiple entities, such as IoT services, other IoT devices or other third parties. The collected data may be sensitive and include personal information. To protect the privacy of the users, the data needs to be protected through an encryption algorithm. For sharing cryptographic cipher-texts with a group of users Attribute-Based Encryption (ABE) is well suited, as it does not require to create group keys. However, the creation of ABE cipher-texts is slow when executed on resource constraint devices, such as IoT sensors. In this paper, we present a modification of an ABE scheme, which not only allows to encrypt data efficiently using ABE but also reduces the size of the cipher-text, that must be transmitted by the sensor. We also show how our modification can be used to realise an instantaneous key revocation mechanism.
Girgenti, Benedetto, Perazzo, Pericle, Vallati, Carlo, Righetti, Francesca, Dini, Gianluca, Anastasi, Giuseppe.  2019.  On the Feasibility of Attribute-Based Encryption on Constrained IoT Devices for Smart Systems. 2019 IEEE International Conference on Smart Computing (SMARTCOMP). :225–232.
The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.